Swallowing accelerometry is a potentially informative adjunct to bedside screening for dysphagia. These measurements are minimally invasive, requiring only the superficial attachment of a sensor anterior to the thyroid notch. Even though single-axis accelerometers were traditionally used for swallowing accelerometry, recent studies have shown that dual-axis accelerometers can capture more of the clinically relevant information. Nevertheless, such measurements are inherently very noisy due to various physiological and motion artifacts. Denoising of dual-axis swallowing accelerometry signals is therefore essential for the development of a robust medical device based on these signals.
Estimation of unknown signals in white Gaussian noise has been dealt with by others. Wavelet denoising has previously been proposed as a valuable option. Wavelet denoising removes the additive white Gaussian noise from a signal by zeroing the wavelet coefficients with small absolute value. The suggested optimal threshold is equal to σε√{square root over (2 log N)}
where σε2 is the variance of the additive noise and N is the length of the signal. This approach requires the knowledge of the noise variance, which can be estimated from the wavelet coefficients at the finest scale. However, wavelet denoising with the suggested optimal threshold does not necessarily produce the optimal results for signals that are not smooth. i.e., signals with noiseless coefficients being of very small amplitude for a large number of basis functions. Recent attempts to overcome this shortcoming have yielded methods that can suffer from high computational complexities for very long signals, and do not necessarily reach the optimal results.
It is an object of this invention to: (1) reduce high computational complexity; and, (2) reduce reconstruction error associated with denoising swallowing accelerometry signals.